Thermal energy dispatch system

ABSTRACT

A system and computer-implemented method for dispatching thermal energy and generating power in a solar power generating plant. The system includes a computer processor, computer readable medium, and control logic stored on the computer readable medium to direct the operation of the processor. The processor generates an optimized thermal energy dispatch schedule which controls operation of the generating plant by performing a combination of direct thermal energy and indirect thermal energy storage simulations to maximize operating revenues.

FIELD OF INVENTION

The present invention relates to solar thermal energy systems, and moreparticularly to a system and method for scheduling thermal energydispatch and power generation in a solar power generating plant.

BACKGROUND OF THE INVENTION

Solar thermal energy systems represent a technology for capturingrenewable radiant energy from the sun and converting that energy intothermal energy that can be used to generate electricity. Concentratingsolar power (CSP) is a technology that offers electric utility scalepower production. CSP systems include collectors such as mirrors orreflectors (sometimes referred to as heliostats or concentrators) thatare arrayed in a solar collector field (“solar field” or “SF”) whichcapture and in turn concentrate sunlight onto a thermal receiver. Thethermal receiver contains a heat transfer fluid such as oil or moltensalt (typically a mixture of 60% sodium nitrate and 40% potassiumnitrate) that is heated to a temperature sufficient to produce steam viaa combination of conventional fluid-to-steam heat exchangers. The steamis used to drive a conventional steam turbine-generator set (“powerblock” or “PB”) which produces electricity that may be sold to a powergrid operated by a an electric power distribution company or utility fordelivery to its customers over a conventional power transmissionnetwork. Some present CSP system designs include parabolic troughsystems, parabolic dish systems, and power tower systems that employ aplurality of reflectors which focus the solar energy onto a thermalreceiver positioned atop a centrally-located tower.

Thermal energy storage (TES) is an integral part of CSP systems forcapturing and storing as much solar thermal energy as possible whenavailable to compensate for periods of time when sunlight is notavailable due to either weather conditions or time of day. TES basicallyemploys an insulated hot storage tank and a pumping and piping systemwith suitable flow control valves which may temporarily store the heatedheat transfer fluid or medium until needed to produce steam forgenerating electricity via the power block. In some systems, acombination of oil and molten salt may be used as the heat transferfluids coupled with a combination of oil-to-salt and/or oil orsalt-to-steam heat exchangers. In other systems, a single heat transferfluid may be used. The heat exchangers are not 100% efficient;therefore, there will be thermal energy losses incurred when heat isexchanged. A typical heat exchanger efficiency without limitation isabout 92% as an illustration. Accordingly, the net amount of thermalenergy that may be either stored in TES or transferred to the powerblock will be less than the thermal energy produced by solar collectorfield.

Two types of TES systems are generally employed—direct storage andindirect storage TES. In direct TES, as shown in FIG. 1, a serialpumping and piping arrangement are used in the CSP system 15 between thesolar collector field 10, thermal energy storage 12 which may compriseone or more conventional insulated storage tanks, and power block 14.Both the solar collector field 10 and thermal energy storage 12 may haveone or more associated pumps that cause the heat transfer fluid ormedium to flow in the desired direction through flow conduits 18A-B (and18C shown in FIG. 2 discussed below). Whenever the solar collector field10 generates thermal energy, it is sent to the TES. If the TES is full,the energy is dumped or wasted. When there is a demand to produceelectric power, thermal energy is drawn from the TES storage tank toproduce steam and drive the turbine-generator set of the power block.The embodiment shown employing dual heat transfer fluids requires bothan oil-to-salt heat exchangers 11 and a salt-to-steam heat exchanger 13.

For indirect TES, as shown in FIG. 2, parallel pumping and pipingarrangements are used in the CSP system 16 between the solar collectorfield 10, thermal storage 12, and power block 14. Therefore, thermalenergy may be routed whenever generated by solar collector fielddirectly to the power block 14 (an via oil-to-steam heat exchanger 17)and/or to the TES (via oil-to-salt heat exchanger 11) depending onwhether there is a demand to produce electric power and/or the amount ofthermal energy needed to produce sufficient steam to drive theturbine-generator set of the power block. The power block may drawthermal energy from both TES (via salt-to-steam heat exchanger 13) anddirectly from the solar collector field if needed.

Both direct and indirect TES have advantages and disadvantages. Theoverall efficiency of indirect TES is higher than direct storage(generally about 8% more in some instances) because two heat exchangesare not always involved in the thermal energy flow between the solarcollector field and power block as shown in FIG. 2. The efficiency ofdirect TES is therefore inherently lower because two heat exchangers arealways used in the system as shown in FIG. 1. However, direct TES has asimpler control system and is generally easier to optimize and scheduleits operation. Optimization is more difficult with indirect TES becauseof its flexible operation since scheduling when to dispatch of thermalenergy from the heated TES tank to the power block depends on the hoursselected for electricity generation. In direct TES, by contrast, thermalenergy must always pass through and be drawn from the heated storagetank whenever there is a demand to produce steam for generatingelectricity. Accordingly, indirect TES requires a more complex controlsystem than direct TES.

Accordingly, an apparatus and method for better optimizing operation ofCSP systems with indirect TES is desired to take advantage of its higherefficiencies in contrast to direct TES.

SUMMARY OF INVENTION

A system and computer-implemented method are provided for optimizing thethermal energy dispatch and power generation in a CSP generating plant.The system generally includes a computer processor, computer readablemedium, and program instructions or control logic stored on the computerreadable medium to direct the operation of the processor. The processorgenerates an optimized thermal energy dispatch schedule which controlsoperation of the generating plant by iteratively performing acombination of direct thermal energy and indirect thermal energy storagesimulations to maximize operating revenues, as further described herein.

BRIEF DESCRIPTION OF THE DRAWINGS

The features of the preferred embodiments will be described withreference to the following drawings where like elements are labeledsimilarly, and in which:

FIG. 1 is a diagram of a direct thermal energy store (TES) system;

FIG. 2 is a diagram of an indirect thermal energy store (TES) system;

FIG. 3 is a perspective view of one embodiment of a microprocessor-basedcomputer and communication system according to the present invention;

FIG. 4 is a chart of prior art qualitative time of use (TOU) data useddispatch thermal energy in a TES generating plant;

FIG. 5 is a flowchart showing an exemplary program instruction/controllogic routine implemented by a computer processor for dispatchingthermal energy in a TES generating plant according to one embodiment ofthe present invention;

FIG. 6 is a flowchart showing an exemplary program instruction/controllogic implemented by a computer processor for determining an optimumoperating window size for a TES generating plant usable in the routineof FIG. 5;

FIG. 7 is a flowchart showing an exemplary program instruction/controllogic implemented by a computer processor for performing a direct TESsimulation for dispatching thermal energy in a TES generating plant;

FIG. 8 is a flowchart showing an exemplary program instruction/controllogic implemented by a computer processor for performing an indirect TESsimulation for dispatching thermal energy in a TES generating plant;

FIG. 9 is a chart showing time of day (TOD) relative energy valuefactors (price adjustment factors);

FIG. 10 is a graph showing the variability in average TOD relativeenergy value factors for a hypothetical month;

FIG. 11 is a graph showing the variability in average TOD relativeenergy value factors price adjustment factors for hypothetical year bymonth;

FIG. 12 is a chart showing TOD relative energy value factors for thegraph of FIG. 11 in tabular form;

FIG. 13 is a chart showing exemplary operating information and datausable in the routine of FIG. 5;

FIG. 14 is a chart showing exemplary results for a first pass from thedirect and indirect TES simulations from the routines of FIGS. 7 and 8,respectively, for dispatching thermal energy in a TES generating plant;

FIG. 15 is a chart showing exemplary results for a second pass from thedirect and indirect TES simulations from the routines of FIGS. 7 and 8,respectively, for dispatching thermal energy in a TES generating plant;and

FIG. 16 is a graph showing the exemplary results from the second passindirect TES simulation of FIG. 15 in graphical form.

DETAILED DESCRIPTION OF THE INVENTION

In the description of embodiments of the invention disclosed herein, anyreference to direction or orientation is merely intended for convenienceof description and is not intended in any way to limit the scope of thepresent invention. Moreover, the features and benefits of the inventionare illustrated by reference to preferred embodiments. Accordingly, theinvention expressly should not be limited to such preferred embodimentsillustrating some possible but non-limiting combination of features thatmay be provided alone or in other combinations of features; the scope ofthe invention being defined by the claims appended hereto.

The decision when to store the energy in the TES, when to send itdirectly to the power block (PB), and when to draw the energy from TESis loosely termed herein as the “Dispatch of Thermal Energy” or simply“Dispatch”. The cumulative results of the foregoing energy routing overa period of time or operating window is termed herein “DispatchSchedule.”

By nature, the thermal energy from a solar field is available only for apart of the day. Generally, the service assurance agreements between thesolar power generator and electric utility dictate that the designer ofCSP plant guarantees certain parameters. They foremost include, but notlimited to, total annual electric power production (measured inmegawatts of energy or MWh). Hence, CSP plants generally are designedwith a solar multiple (SM) of greater than 1.0 and thermal energystorage (TES) to allow the plant to generate power even during inclementweather and at night. SM is basically the ratio of the thermal capacityof the collector field to the thermal energy requirements of the steamturbine-generator set. The excess thermal energy delivered by the solarcollector field (SCF) is therefore stored in TES and used to generateelectricity even when the sun is not shining.

The decision when to store the thermal energy in the TES, when to sendthe energy directly to the power block (PB), and when to draw the energyfrom TES is loosely termed as the “Dispatch of Thermal Energy” or simply“Dispatch.”

More often than not, electricity users or customers of the utilitypurchasing power are charged a variable rate for the electricity. Thisis based on the season (typically monthly), day of the week (weekday orweekend) and the hour of the day. For example, in many instances,periods of peak electric demand often fall during the early morning orevening hours. And depending on where the utility is located, winterand/or summer may be seasons of peak demand commanding higher electricrates due to high HVAC loads whereas spring or fall may be lower.

Generally, a qualitative term referred to as “time of use” (TOU) hasbeen used heretofore to represent the variability in electricity ratesand periods of demand. TOU is a discrete number graded on a scale of 1to 10, with 1 representing the most profitable period to dispatchthermal energy and generate power and 10 relatively being the leastprofitable. Representative average monthly TOU data for a hypotheticalmonth of January is shown in FIG. 4. This TOU data was used heretoforeto determine thermal energy dispatch and power generation schedulingpriorities with the implicit assumption being that dispatch should meetthe requirements of TOU=1 first, then 2, 3, and so on.

Thermal energy dispatch and power generation scheduling decisions andoptimizations of the past that have been based on TOU lack themathematical precision required to make a fully informed decisionsrequired to fine tune and optimize revenues for the CSP generatingplants. This is particularly applicable to CSP with indirect TES whichhas the operational flexibility described herein to use thermal energydirectly generate power and/or store the energy in TES for later use.Although TOU describes relative revenues and priorities for powergeneration and dispatch of thermal energy, TOU is not a quantitativerevenue metric and therefore does not convey the actual revenue that canpotentially be realized by scheduling or routing thermal energy dispatcheither directly to the power block, to TES, or combination of both.Accordingly, there may be times for example during off-peak power demandperiods, but when prime solar thermal energy production is at itsgreatest, where it may be more profitable to store the thermal energy inTES (assuming sufficient capacity remains) rather than routing theenergy to the power block to produce electricity when the utility ispaying the least for the power produced by the CSP.

In short, the CSP plant controller prior to the present invention hasgenerally used a qualitative heuristic or ad hoc method together withimprecise TOU information to schedule the dispatch of thermal energy andelectric power generation rather than applying a more systemic andquantitative approach geared to maximize revenue based on factors suchas the solar field collector configuration, weather predictions, storagecapacities/limits of the TES, heat exchanger and corresponding CSPsystem efficiencies in an indirect TES arrangement, and fluctuatinghourly, monthly, and seasonal price of electricity paid by the utilityto the CSP power generator based on variations in the projected utilitycustomer demand for electricity. Further, when CSP plants are initiallydesigned and built, using TOU projections does not help to design theplant optimally. More often than not, this results in over design (e.g.over-sizing of the TES tanks) thereby increasing initial capital costsand levelized cost of energy (LCOE) prices (i.e. $/kWh).

According to one aspect of the present invention, a system and method isprovided for optimizing the thermal energy dispatch and power generationof a CSP plant 16 with indirect TES shown in FIG. 2.

Thermal Energy Dispatch System

FIG. 3 is a diagram of one embodiment of a thermal energy dispatchsystem 20 for dispatching thermal energy in a CSP generation plant 16configured with indirect TES. Dispatch system 20 is a computer-baseddata processing system and communication network that may include one ormore conventional micro-processor based computers and/or servers thatare operable to transmit and exchange data to operate the system andoptimize the scheduling and routing of thermal energy between the solarcollector field 10, thermal storage 12, and/or power block 14 (see FIG.2). Dispatch system 20 includes a primary thermal energy dispatchcomputer or controller 30 having suitable memory that acts as aprocessing hub, associated local or remote database(s) 40 accessible tothe controller, a plurality of two-way data communication links 80, anda plurality of two-way communication interfaces 70 interconnecting amultitude of possible subsystems 100-107 all operably linked to the maindispatch controller 30 as shown in FIG. 3. Data communication links 80may transmit/exchange data, control commands, and signals electronicallybetween the dispatch controller 30 and various subsystems 100-107 whichmay be local or remote to the dispatch controller and/or the CSP plant.The data communication links 80 may include without limitationconventional wireless, wired, “on-board” (circuit board) conductors,Internet, and combinations thereof.

With continuing reference to FIG. 3, dispatch controller 30 may be aconventional commercially-available computer or networked computersincluding a central processing unit (CPU) or processor and optionallyinclude ancillary on-board or accessible processors, all of which arepre-programmed with software or computer programs that implement programinstructions or control logic routines operable to direct the controllerto perform the calculations and data analysis necessary to schedule anddirect thermal energy dispatch and power generation in the CSPgenerating plant and/or control various plant subsystems 100-107 as maybe required. It will be appreciated that the program instructions orcontrol logic may be implemented in hardware, firmware, software, or anycombination thereof. The program instructions or control logic may bestored and encoded on any suitable commercially available computerreadable medium and is readable by dispatch controller 30 forimplementation thereby.

The computer readable medium suitable for use with dispatch system 20may include any type of volatile or non-volatile media such as withoutlimitation computer memory of any type (e.g. any type of RAM, ROM, flashmemory, memory cards or chips, etc.) and magnetic, magneto-optical, oroptical media, discs, or tapes (e.g. hard disks, CD, DVD, magnetic tape,etc.).

Database 40 may reside on any conventional type of computer readablemedium or data storage device that is accessible to dispatch systemcontroller 30.

In some embodiments, with reference to FIGS. 2 and 3, the subsystems100-107 which interface with dispatch controller 30 via datacommunication links 80 may include a weather station subsystem 100,weather prediction services subsystem 101, solar collector fieldsubsystem 102 associated with solar collection field 10, TES subsystem103 associated with thermal storage 12, power block subsystem 104,oil-to-steam heat exchanger subsystem 105 associated with oil-to-steamheat exchanger 17, salt-to-steam heat exchanger subsystem 106 associatedwith salt-to-steam heat exchanger 13, and oil-to-steam heat exchangersubsystem 107 associated with oil-to-salt heat exchanger 11.

Weather prediction services subsystem 101 may be a remote third-partyservice such as NOAA (National Oceanic and Atmospheric Administration)having accessible databases with weather-related data useful in makingweather forecasts and scheduling thermal energy dispatch for the CSPgenerating plant. Local weather station subsystem 100 preferably is anon-site weather monitoring system that can measure and providecontemporaneous data on changing local weather conditions.

As further described herein, it will be appreciated that weather ormeteorological data is a key data component used in the method describedherein to optimize scheduling of thermal energy dispatch/transfer andpower generation in a CSP plant with indirect TES. This weather dataincludes current local weather conditions and weather predictions orforecasts for the coming period as provided from subsystems 100 and 101,respectively. The weather data is useful for determining whether theupcoming period of time will provide sufficient sunlight for the solarcollector field to produce thermal energy which may stored in TES and/orsupplied directly to the power block for generating power. Accordingly,in some embodiments, dispatch controller 30 or other processors indispatch system 20 may be programmed to periodically access and downloadthe most current weather data and information from these subsystems 100and 101 on a periodic basis. In other embodiments, the weather data andinformation may be monitored and downloaded to dispatch system 20 on asubstantially continuous basis. This weather data may be stored indatabase 40 or elsewhere in dispatch system 20 such as in dispatchcontroller 30 and retrieved by the dispatch controller to perform thethermal energy dispatch and power generation schedule optimization.

With continuing reference to FIG. 3, dispatch system 20 includes a userinterface device 60 and an associated graphical user interface (GUI) 50.In some embodiments, interface device 60 and GUI 50 may be aconventional computer terminal with graphic display or monitor thatallows a user or plant operator to upload and download data and controlcommands to/from the dispatch controller 30. This data may includeprogrammable control logic and software used to control operation anddispatch of thermal energy in the CSP generating plant. The userinterface device 60 and GUI 50 in some possible embodiments may be adesktop PC, laptop, notebook computer, PDA, Blackberry, cellulartelephone or other suitable system access device that allows the user toprogram and alter operation of the CSP plant either locally or from aremote location via wired or wireless data communication links 80.

It is well within the ambit of those skilled in the art to determinesuitable types of processor and speeds, hard disk storage, memory, powersupply, and other typical ancillary equipment and performancerequirements needed for the computer-based thermal energy dispatchsystem 20 described herein depending on the specific applicationrequirements at hand.

Thermal Energy Dispatch and Power Generating Schedule Optimization

One embodiment of a computer-implemented method for optimizing thethermal energy dispatch and generating hour scheduling in CSP powergenerating plants with TES will now be described. In one embodiment, theCSP generating plant used indirect TES. Preferably, the methodadvantageously combines and iteratively performs both direct andindirect based TES operating simulations and analyses. In general, apreliminary or initial thermal energy dispatch schedule and sequence isfirst developed, and then further refined through iteratively performingdirect and indirect TES simulations to generate a final thermal energydispatch schedule that optimizes both thermal energy utilization in theCSP plant with indirect TES and maximizes generating revenue. The finaldispatch schedule is programmed into a control system to direct theoperation of the TES and generating plant. According to the programmeddispatch schedule, the control system automatically regulates the flowof thermal energy in the TES generating plant by opening/closing flowconduits via valving or similar to route thermal energy from the solarcollector field to either TES and/or the power block, and from TES tothe power block in some situations. In a preferred embodiment, thecontrol system is a computer processor based control system such asdispatch controller 30 described herein the method that automaticallyexecutes the thermal energy dispatch schedule.

Reference is made to FIGS. 1-3, 5, and 10. Referring now to FIG. 5,which shows the main control logic process or routine 200 executed bydispatch controller 30, the thermal energy dispatch and schedulingoptimization method preferably may include the steps that follow below.These steps represent programmable control logic or routine instructionsteps that are pre-programmed into and executed by dispatch controller30. The control logic instruction steps may be stored in suitablecomputer readable medium or memory operably associated with andaccessible to controller 30.

The thermal energy dispatch and scheduling optimization method beginswith reference to FIG. 5 and the following control logic steps inroutine 200:

Initialize and reset the dispatch system 20 including dispatchcontroller 30 to being a new simulation (Step 202).

Load or program the dispatch system 20 with operating information anddata required by dispatch controller 30 to execute the applicable dataanalysis and control logic steps described herein (Step 204). The inputoperating data preferably includes operational and financial informationthat may include without limitation: plant design/operatingcharacteristics (e.g., nameplate rating, heat exchanger and otherefficiencies, etc.), required revenue profiles (further explainedelsewhere herein), initial TES value, operational policy guidelines(e.g. minimum guaranteed electricity production), operator overrides,operational and/or financial limits, local actual weather data fromweather station subsystem 100, weather predictions for the comingoperating window or period from weather prediction services 101, and anyother information and data that may be relevant to CSP generating plantand analysis for when to dispatch thermal energy and generateelectricity. FIG. 13 shows representative operating information and datathat may be loaded into dispatch controller 30 in Step 204 for aninitial default operating window of 24 hours.

Load or program the system with TMY (Typical Meteorological Year) datafor the previous 8 years, in typical meteorological (MET) year dataformat as will be recognized by those skilled in the art (Step 206).

Set the TES with the specified initial value (Step 208). In the firstrun through logic routine 200, the initial energy reserve available inTES for use by the Power Block (PB) will be set to zero.

Retrieve the weather prediction data for the next specified period (Step210).

Calculate and determine the CSP plant operating window size that will beused by the following control logic routine steps for optimizing thethermal energy dispatch or transfer schedule and power generationschedule to maximize revenue production for the power generator (Step212). The operating window size is measured in units of time, such as aperiod of hours in some embodiments. The determination of theappropriate operating window size to use preferably is based on datasuch as without limitation weather predictions for the upcomingoperating period, revenue profiles, and operating policies. An initialdefault window size of 24 hours may be used which follows the diurnalcycle; however, the window size is automatically adjusted to a longerperiod of time by the dispatch system 20 in some embodiments based datasuch as weather data and revenue profiles as further described herein.

The operating window sizing programmable control logic routine 300executed by dispatch controller 30 will be described in more detailbelow with reference to FIG. 6 after fully describing the thermal energydispatch and power generating schedule optimization process.

Pass-1, Step-1 Optimization

Next, in main control logic Step 214 shown in FIG. 5, the thermal energydispatch is optimized using a Direct Storage assumption and simulation(Pass-1, Step-1). The Pass-1, Step 1 optimization control logic routine400 will now be further described with reference to FIG. 7 and Steps402-430 shown therein, and includes:

Retrieve and load the weather prediction for the required period (e.g.from subsystem 101) into dispatch controller 30 (Step 402).

Based on the predicted weather, estimate the amount of thermal energyexpected to be generated by the Solar Collector Field for each hour forthe required optimization window period of time that has preferably beenpre-set in dispatch controller 30 (Step 404).

Add the thermal energy generated by the solar field to the thermalstorage, preferably after taking the oil-to-salt heat exchangerefficiency into account (Step 406).

Initialize all the hours to “Not-Excluded” for power generation (Step408).

Examine the relative revenue profile (explained elsewhere herein)pre-loaded into dispatch system 20 and select or pick the nextgenerating hour (Step 410). The next generating hour is defined hereinas the hour for which there is sufficient energy available in TES foroperating the power block that will provide maximum revenue and will notaffect an already planned generating hour in the dispatch schedule.

Determine if that selected generating hour in Step 412 negates thealready planned TES dispatch and generating schedule.

If “YES” is returned in Step 410, then mark that selected hour as“Excluded for this cycle” and continue the search for next bestgenerating hour (Step 411).

If “NO” is returned in Step 412, then select this selected hour (Step414).

Next, determine or check whether electric power is being generated inthe previous hour to the selected hour (Step 416). If “YES” is returnedin Step 416, then mark this selected hour as a “non-startup hour.” If a“NO” is returned, then mark this selected hour as a “startup hour.” Inthis context, “startup hour” means that the power block (PB) was cold(i.e. offline) and therefore consumes more energy during startup untilthe PB is ramped up to and reaches normal optimum turbine-generator setoperating temperatures and pressures for generating electricity than a“normal or non-startup hour” wherein the PB is already operating atnormal operating conditions. Accordingly, the efficiency of the PB (i.e.turbine-generator set) is greater at normal operating temperatures thanduring the ramp up period from cold start. Whether the selected hour isa startup or non-startup hour sets the amount of energy the PB requiresduring that hour from the SF and/or TES, with a startup hour requiringmore energy.

Next, in Step 418, reduce the amount of thermal energy in TES (aftertaking into account the salt-to-steam exchange efficiency) by the amountof energy needed for the power block to generate the required electricpower.

Next, determine if there is enough thermal energy in TES to add anothergenerating hour (Step 420). If “YES,” then repeat above Steps 410through 418 [7.e through 7.i]. Steps 410-418 are repeated until anothergenerating hour cannot be added (i.e. a “NO” is returned in Step 420which indicates that there is not enough thermal energy in TES to addanother full generating hour).

After a “NO” has been returned in prior Step 420, compute the energyremaining in TES at the end of the optimization period (Step 422). Thiswill be the starting value used for TES for the next optimizing period.

Next, compute the total energy used by PB (Step 424).

Compute the total electricity generated by the PB and compute thecorresponding revenue generated (Step 426).

Store the schedule in database 40 or elsewhere for further processing,records, and later audits (Step 428).

Determine the energy transfers from Solar Field to TES and TES to PB(Step 430).

This completes and ends the optimization routine 400 for Direct Storage(Pass-1, Step-1) corresponding to Main control logic Step 214 in FIG. 5.

Pass-1, Step-2 Optimization

With reference now to Step 216 shown in main control logic routine 200in FIG. 5, the resulting data and schedule obtained in the above DirectStorage optimization control logic routine 400 (Pass-1, Step-1) are nextoptimized to meet the “Indirect Storage” assumption (Pass-1, Step-2).Step 216 thus provides an additional fine-tuning or refinement ofresults obtained in control logic routine 400 by now updating thethermal energy dispatch and power generating schedule assuming indirectthermal energy storage.

The Pass-1, Step-2 optimization control logic routine 500 will now befurther described with reference to FIG. 8 and Steps 502 though 522shown therein. In general, each generating hour determined and analyzedin the thermal energy dispatch and generating schedule from foregoingcontrol logic routine 400 (Pass-1, Step 1) is now examined to determinewhether there is any energy generated by the solar field (SF) duringeach hour. In contrast to direct TES, thermal energy generated by thesolar field in an indirect TES CSP generating plant may be eitherdispatched directly to the power block (PB) and/or to TES for later use.

Referring to FIG. 8, routine 500 begins with Step 502 by setting andselecting the first thermal energy dispatch and generating schedule hour(e.g. hour=1) from Pass-1, Step-1 (Step 214 in FIG. 5).

Examine this first generating hour and determine whether there is anythermal energy generated by the solar field during this generating hour(Step 504).

If a “YES” is returned wherein the thermal energy available from theSolar Field is greater than the PB requirements (after taking theoil-to-steam heat exchange efficiency into account) then the followingsteps are executed by dispatch controller 30:

Update the Solar Field to Power Block (SF-to-PB) value to draw the fullamount of thermal energy required by the PB directly from the SolarField (Step 514);

Reduce the amount drawn from TES (i.e. TES to Power Block PB) by anequivalent amount because the PB thermal energy requirements are beingsupplied directly from the Solar Field so no TES reserves are requiredto generate electricity (Step 516); and

Add any excess or remaining thermal energy produced by the Solar Field(i.e. thermal energy not required by the PB to generate power) to TES(Step 518). It should be noted that there will generally always be a netsavings in TES because the salt-to-steam heat exchanger efficiencyinvolved with drawing thermal energy from TES to the PB is eliminated(i.e., all the thermal energy required by the PB comes directly from SFto the PB).

Alternatively, if a “NO” is returned in Step 506 wherein the Solar Fieldto TES (SF-to-TE) is less than the PB requirements for generatingelectric power for that first generating hour being analyzed, then thefollowing steps are executed by dispatch controller 30:

Route whatever thermal energy is available from the SF directly to thePB (Step 508);

Withdraw the rest of the PB thermal energy requirements (i.e. thedifference or shortfall in thermal energy that cannot be provideddirectly from the SF during that first hour) from TES reserves and routethe withdrawn thermal energy to the PB (Step 510); and

Adjust (i.e. decrease) the TES value of thermal energy reserves toreflect the corresponding reduction in the available TES by an amountequivalent to the thermal energy withdrawn in Step 510 and routed to thePB (Step 512). It should be noted that this would result in a netsavings because a part of the energy transfer is directly between SF andPB (i.e. it does not involve TES-to-PB salt-to-steam heat exchangerefficiency).

Next, in Step 520, the first generating hour analyzed is incremented andaccounted for whether the result of the decision in Step 506 was “YES”or “NO”. Then, in Step 522, the control logic routine 500 determines ifall the generating hours from the Direct Storage control logic routine400 have been analyzed under the Step-1, Pass-2 Indirect Storageassumption. If “NO” is returned, Step 506 is repeated again until allgenerating hours in the schedule have been analyzed under routine 500.If “YES” is returned, routine 500 of Step-1, Pass-2 is terminated.

It should be noted that in the end of the foregoing indirect TESoptimization in routine 500 (Step-1, Pass-2), the TES will have moreenergy remaining or reserves when compared to the direct TESoptimization (e.g. Steps 1-7) because thermal energy to the PB flowsboth directly from the Solar Field and from TES to meet the full thermalenergy requirements of the PB (i.e. dual thermal energy feeds to the PBas shown in FIG. 2). It is interesting to note that the maximum capacityof thermal storage 12 needed for indirect TES will be larger than thatneeded for direct TES because the thermal storage 12 is sized toaccumulate excess thermal energy from the Solar Field when not all theenergy produced by the Solar Field is needed to satisfy the requirementsof the PB.

Pass-2, Step-1 Optimization

Referring back now to main control logic process 200 in FIG. 5, usingthe resulting new data and dispatch schedule obtained from the abovePass-1, Step-2 Indirect Storage assumption optimization in control logicroutine 500, we next re-optimize the dispatch for Direct Storage(Pass-2, Step-1) in Step 218. Initially, it should be noted that thereis more thermal energy available in the TES at the end of foregoingPass-1, Step-2 Indirect Storage optimization (control logic process 500)than the energy remaining in TES in Pass-1, Step-1 Direct Storageoptimization (control logic process 400) since part of the PB thermalenergy requirements are satisfied directly from the Solar Field inIndirect Storage.

In the Pass-2, Step-1 (Step 218) optimization now, we try adding moregenerating hours to the dispatch schedule using the new value ofremaining energy in TES calculated due to indirect storage (Pass-1,Step-2; logic process 500). In one embodiment, therefore, the sameforegoing “add a generating hour” analysis sequence of Steps 410 through420 in the Direct Storage control logic routine 400 (Pass-1, Step-1) isnow repeated to determine if more generating hours may be added. In mostcases, it is expected that more generating hours can be added to theschedule except perhaps in a special case where the generation ofelectricity using PB does not overlap at all with the thermal energyavailable from the Solar Field (e.g., all the hours of generation are inthe overnight dark hours). The Pass-2, Step-1 optimizationadvantageously will typically result in higher revenue from the same CSPplant because the thermal energy reserves in thermal storage 12 (see,e.g. FIG. 2) are better utilized.

Pass-2, Step-2 Optimization

Using the resulting data and dispatch schedule obtained above in Pass-2,Step-1, the results for Indirect Storage are next re-optimized (Pass-2,Step-2) in Step 220 of main control logic routine 200 (see FIG. 5) usingthe following steps:

First, the same foregoing Indirect Storage optimization method describedin Pass-1, Step-2 is repeated. Accordingly, in one embodiment, controllogic routine 500 including Steps 502 through 522 are run again but thistime using the results of the new schedule generated in above Pass-2,Step-1 of Direct Storage. Any remaining thermal energy remaining in TESat the end is marked and used as the starting value in TES for nextoptimization period for executing main control logic routine 200 again.

At the completion of Pass-2, Step-2, the projected electricity generatedand revenue to be produced is computed.

Returning now again to FIG. 5, at the completion of Pass-2, Step-1, maincontrol logic process 200 continues in Step 222 in which the finaloptimized thermal energy dispatch and CSP plant generating schedule isstored in dispatch controller 30 and its associated memory and/ordatabase 40. In step 224, the dispatch controller 30 is programmed withthe final thermal energy dispatch and generation schedule. In step 226,the operating window size is incremented and set to the windowdetermined in control logic routine 300 shown in FIG. 6 and describedimmediately below. In one embodiment, the operating window size may beat least 24 hours which may represent a default operating window sizethat follows the diurnal cycle. In some embodiments, the operatingwindow size may be greater than 24 hours. In such cases, the first 24hours of the dispatch schedule may be retained and a sliding 24 hours isused for the next optimization.

According to the sequencing and timing developed in the final thermalenergy dispatch schedule programmed into dispatch controller 30, thecontroller will open/close flow conduits 18A-C via valving andstart/stop various pumps associated with solar collector field 10 andthermal storage 12 in the thermal energy pumping and piping system asshown in FIG. 2 at predetermined times in a manner which advantageouslyoptimizes thermal energy dispatch in the TES generating plant andmaximizes revenue production for the generating plant. Dispatchcontroller 30 is operative to generate and transmit control signals tothe thermal energy pumping and piping system valving and pumps in aconventional manner via suitable data communication links similar tolinks 80 described herein to effectuate control of that equipment. Anexemplary final thermal energy dispatch schedule showing the thermalenergy flow control and sequencing through a TES generating plant isfurther described elsewhere herein with reference to FIGS. 14-16.

Adaptive Optimization Window of Time

According to another aspect of the invention, a method is provided forcreating an adaptive optimization window or period of time that is usedin the thermal energy dispatch and power generation scheduling method inmain control logic process 200 described above. The optimization windowpreferably adapts to both changing weather conditions and changingrevenue profiles to generate an overall operating schedule (i.e. boththermal energy dispatch and power generation) for a CSP plant withindirect TES over a variable period of time that maximizes powergeneration revenue. In some embodiments, the optimization window may beextended to maximize power sale revenues for the CSP plant.

Due to the nature of solar power generation using CSP, the initialoptimization window size defaults to diurnal cycle. Hence, the dispatchsystem 20 preferably uses an initial default optimization window size of24 hours for calculating the schedule used to control the thermal energydispatch or transfer and electric power generation (i.e. operation ofthe turbine-generator set in the power block 14).

Referring to FIGS. 3 and 6, one embodiment of a method for creating anadaptive optimization window is shown in control logic routine 300 thatmay be executed by the dispatch controller 30. Logic routine 300 mayinclude some or preferably all of the follow steps:

In Step 302, set the initial optimization window to a default period of24 hours.

Next, in Step 304, the weather prediction data downloaded into dispatchsystem 20 for the upcoming next 24 hour initial default period, such asfrom weather prediction services sub-system 101 for example, is accessedand retrieved by dispatch controller 30. This data may be stored inmemory, database 40 or elsewhere in the system and retrieved by dispatchcontroller 30. From the weather prediction data, the amount of thermalenergy that would be available from the solar collector field (SF) forpower generation based on anticipated weather conditions during the 24hours period is estimated and analyzed.

In Step 305, access and retrieve the relevant revenue profilepre-programmed into dispatch system 20 for the pertinent day, month, and24 hour period or window being analyzed and compare the revenue profileto the solar energy available from the SF during the period or window inquestion.

In Step 306, determine the “X” best revenue hours of the 24-hour windowfrom a maximum revenue standpoint, wherein X=a representative totalnumber of predetermined individual revenue hours during the initialdefault window selected for conducting the optimization analysis andwhich are pre-programmed into dispatch controller 30. In one preferredembodiment, a total of 6 hours (i.e. X=6) may be selected for the “X”best revenue hours for example.

Next, in Step 308, compute the average of the 6 best revenue hours(“X-average”). For example, the 6 best revenue hours may fall on Hours6, 7, 8, 9, 10, 11, and 12 during the 24 hour initial window beinganalyzed, which coincides with the hours during which the electricutility is willing to pay the most for each MWe of electricity generatedby the CSP generating plant. The average of these hours calculated bydispatch controller 30 is therefore Hour 10.5, which represents anaverage hour during the initial default window when revenues would be ata maximum.

In Step 310, determine the “Y” best solar field (SF) thermal energyproduction hours from a maximum solar energy available standpoint,wherein Y=a representative total number of predetermined individualsolar hours during the default window selected for conducting theoptimization analysis and which pre-programmed into dispatch controller30. Preferably, “Y” should equal “X” to provide a consistent basis forcomparing maximum revenue hours and solar thermal energy productionhours as further described below. Continuing with the foregoingexemplary case where X=6 hours as described above, Y=6 best solar fieldhours in this embodiment.

Next, in Step 312, dispatch controller 30 computes the average thermalenergy available during the 6 best solar field thermal energy productionhours (“Y-average”). Continuing with the foregoing example, the 6 bestsolar production hours may be Hours 12, 13, 14, 15, 16, 17, and 18. Theaverage of these hours is therefore Hour 17.5, which represents anaverage hour during the initial default window when solar energyproduction would be at a maximum.

It will be appreciated that either the “X” or “Y” best revenue and solarproduction hours, respectively need not, and often will not be purelyconsecutive hours as shown in the foregoing examples due to variabilityin daily and weekly revenue profiles and available of the sun to producethermal energy.

Then, in Step 314, a test is performed to compare and determine if theaverage revenue hours (“X average”) are less than the average solarfield hours (“Y average”) from Steps 310 and 312, respectively. CSPgenerating plants with TES have the ability to capture and store thermalenergy during one period of time based on solar availability, and thenlater use the stored energy during a subsequent or later period of timewhen it may be financially more advantageous to use the thermal energyto generate and sell electricity to the utility company as explainedherein. Based on this premise and to take full advantage of TES, Step314 is therefore intended to generate a optimum thermal energy dispatchwindow of time in which the average peak hours that solar energy isavailable from the sun to produce thermal energy (based on the sunshining due to good weather conditions and time of day) occurs beforethe thermal energy is needed to meet the utility's peak power demandperiod which coincides with the maximum or best revenue production hours(i.e. when the utility is willing to pay most for electricity generatedby the CSP plant based on the TOD relative energy value factorsdescribed herein). Ideally, therefore, the maximum solar and thermalenergy production hours during the initial default window selected foranalysis preferably should not occur after the hours when the powerdemand is the greatest.

Accordingly, with continuing reference to Step 314, if the average ofthe revenue hours (X average) is equal to or greater than Solar Fieldhours (i.e. a “NO” response is returned to the test), control passes toStep 320 in which the optimization window size is set to the currentwindow size under analysis. This window size is then used by maincontrol logic routine 200 as described herein with reference to FIG. 5,Step 212. In this case, the “NO” response in Step 314 signals thepreferred situation in which thermal energy production peaks before themaximum revenue and power demand period as described above.

If alternatively the average of the revenue hours is less than SolarField hours (i.e. a “YES” response is returned) in Step 314, then a newlarger window size is created in Step 316 because the thermal energyproduction peaks after the maximum revenue and power demand periodduring the default window. This means that the solar energy availablewould not be used to its optimum benefit to produce and store thermalenergy for later use to generate power. In one exemplary embodiment, anadditional 24 hours is added to the original 24 hour window size beinganalyzed in Step 316 which therefore is doubled to a new window of 48hours for further analysis in routine 300. In addition, the number ofbest revenue hours and thermal energy production hours by the solarfield selected in Steps 306 and 310, respectively, may also be doubledto coincide with the new larger 48 hour window being analyzed. In theforegoing example, the number of hours analyzed for the new 48 hourswindow being analyzed by dispatch controller 30 may therefore be 12hours in lieu of the 6 hours originally analyzed for the initial default24 hour window.

Continuing with the foregoing example to illustrate an exemplary testperformed in Step 314, Hour 10.5 from Step 308 representing the averageof the 6 best revenue hours (X average) would be compared to Hour 17.5from Step 310 representing the average of the 6 best solar productionhours (Y average). In this example, the average revenue hour (i.e. Hour10.5) is less than the average solar production hour (i.e. Hour 17.5)meaning peak power demand unfortunately occurs before the chance toproduce and store thermal energy. Therefore, a “YES” response would beproduced in Step 314 in this example and control would pass to Step 316described above wherein the subsequent window size (e.g. 48 hours insome embodiments) is doubled to re-run the optimization window analysisin hopes of generating a “NO” response in Step 314 during the next passthrough control logic routine 300.

In Step 318, logic routine 300 continues and a test is performed tocompare the new window size determined in Step 316 to a maximumpredetermined window size limit that is input into dispatch controller30. In one embodiment, for example, the maximum window size may be 240hours (10 days). However, any suitable operating window size may beused. If the new window size is less than or equal to the new windowsize limit (i.e. a “NO” response is returned), foregoing Steps 304through 316 are repeated until the average of the best revenue hours isgreater than or equal to the average of solar field hours in the test ofStep 314, or the window reaches the limit in Step 318 as alreadydescribed herein.

Preferably, in one embodiment, if the operating window for optimizingrevenue is greater than 24 hours, then the first 24 hours of thegenerating schedule is retained and the sliding of 24 hours is used fornext optimization cycle in control logic routine 300. As an example, ifthe foregoing thermal energy dispatch window optimization yields anoptimum window size of 96 hours and the initial hour of the schedulebeing analyzed starts at schedule Hour 4001, the thermal energy dispatchschedule will be based on a 96 hour window from Hours 4001 to 4096. Thethermal energy dispatch schedule for the first 24 hours of the schedulefrom Hours 4001 to 4025 is retained by dispatch controller 30 andthermal energy will be dispatched or routed in the CSP generating plantaccordingly by dispatch controller 30. The next optimization analysisperformed by control logic routine 300 will start at Hour 4025 andproceed as described above starting with an analysis for the next 24hour period from Hour 4025-4048.

It should be noted that some utility companies pay more for the powergenerated in the early morning hours since that is a peak electricconsumption period for utility customers. Some utilities even mandatethat the CSP generating plant produce and sell electricity during thesemorning hours, even though due to weather conditions this may not be themost efficient or profitable time for the CSP generator to generatepower with indirect TES if the solar field cannot directly provide atleast some of the thermal energy required by the power block.Advantageously, the proposed adaptive window selection method willhandle these cases in a natural way by factoring the weather conditionsand maximum revenue generating profiles into the modeling and adaptingthe window to a longer period of time than the next 24-hour periodduring which time inclement weather may be experienced.

Revenue Profiles

Revenue profile data, which is retrieved and processed by the presentforegoing method and transformed into actual schedules for operating andcontrolling thermal energy dispatch and electric generation in the CSPplant, represents the relative hourly, daily, monthly, and seasonalfluctuating prices (e.g. $/kWh) that the electric utility is willing topay the CSP power generator for electricity based on the historicalfluctuation in electric demands of the utility's customers. Agreementsbetween independent CSP power generators and utilities stipulate andgenerally guarantee what revenue the utility can expect to receive fromthe utility for power sold. This power sale revenue is determined fromthe product of (1) a base energy rate (also referred to as levelizedcost of energy or LCOE) for the CSP plant and (2) a “time of day (TOD)price adjustment factor” that reflects the time-dependent variability inelectric demand. An example of representative time of day priceadjustment factors is shown in FIG. 9. The days of the month are shownin Row 4 across the top of the chart and the hours of the day are shownin Column A. It should be noted that the base energy rate will be uniquefor each CSP generating plant and reflects both capital and O&M(operating and maintenance) costs pertinent to that plant.

As seen in FIG. 9, there are times when the CSP generating plant willreceive its full base energy rate during normal electric demand periods(i.e. “1.00”), less than the base energy rate during low demand periods(e.g. 0.5 or 0.75 times the rate), or more than the base energy rateduring peak demand periods (e.g. 1.25 or 1.5 times the rate).Accordingly, it is more profitable for the CSP generating plant togenerate and sell power to the utility during period of normal or peakelectric demand, rather than during low demand periods when the CSPplant will sell generate and sell power at a loss (i.e. less than thebase energy rate).

Examples of a representative monthly and yearly revenue profiles aregraphically depicted in FIGS. 10 and 11. FIG. 10 shows a possibleaverage monthly-hourly revenue profile for January which has beenaveraged for each day over the entire month. The time of day (TOD)factors described herein are shown on the vertical axis and GeneratingHours are shown on the horizontal axis. However, it will be appreciatedthat individual daily-hourly revenue profiles may be generated and usedwith the hourly variations in revenue for each day of the entire month.FIG. 11 represents a cumulative graph that includes all 12 averagemonthly-hourly revenue profiles for a given year. The data representedby the graphs or curves in these figures is input and downloaded intodispatch controller 30 for each hour of each day during the analysisperiod for use in the thermal energy dispatch and power generationoptimization processes and control logic routines described herein.

FIG. 12 shows the individual average monthly-hourly revenue data forJanuary through December that is graphically depicted in FIG. 6 intabular form as pre-programmed and loaded into dispatch controller 30.The days of the month are shown in Row 3 across the top of the chart andthe hours of the day are shown in Column A.

The revenue profiles advantageously provides precise predictions ofactual revenues that the CSP power generator can expect from sellingelectricity to the utility over a given period of time, unlike theimprecise relative scales used in past based on TOUs (e.g. numbers onscale of 1 to 10) as described above. Revenue profile data and exemplaryprofiles that may be used in the method of the present invention aredescribed in more detail herein.

The revenue profile date is pre-programmed into dispatch system 20 andmay be stored in database 40 on computer-readable medium or resideelsewhere in the system for later access during the thermal energydispatch and power generation optimization process. It will beappreciated that using the more detailed and fine tuned revenue profilein lieu of TOU integer data heretofore will allow more precisecalculation of expected CSP plant revenues from power sales tofacilitate developing the optimum thermal energy dispatch and powergeneration schedules to maximize revenues. In some instances, suchschedules may dictate storing available from the solar collector field10 in lieu of generating power in the power block 14 (see FIG. 2) duringperiods of low electric demand, and reclaiming the thermal energy fromTES 12 to generate power at a later time during normal or peak periodsof electric demand.

FIGS. 14 and 15 show exemplary results in tabular form of the maincontrol logic routine 200 of FIG. 5 for a 24 hour operating window. FIG.14 shows the results of the first Pass (i.e. Pass-1, Steps 1 Direct TESand 2 Indirect TES). FIG. 15 shows the results of the final second Pass(i.e. Pass-2, Steps 1 Direct TES and 2 Indirect TES), with the bottomchart on FIG. 15 representing the results of the final thermal energydispatch and power generating schedule for the 24 hours period shown.The system and method according to the present invention advantageouslycombines both Direct and Indirect TES models and assumptions usingmultiple passes to fine tune the dispatch/generating schedule andmaximize generating revenues.

FIG. 16 graphically depicts the results of the final 2nd Pass Indirectanalysis shown in the bottom table on FIG. 15 (note that in FIG. 15,data point 1 on the horizontal axis=generating Hour 4345 and data point24=generating Hour 4368). Thermal energy units shown in the leftvertical axis are in MWh.

Referring to FIGS. 15 and 16, data point 17 in FIG. 16 corresponding togenerating Hour 4361 (index #16) in the bottom chart on FIG. 15 (2ndPass-Indirect Storage) will be used as an example to illustrate theresults from the present thermal energy dispatch and power generationoptimization system and process described herein. During Hour 4361, aprojected 911 MWh of solar energy will be available and produced by thesolar collector field (SF-TE). The amount of energy required and used bythe Power Block (PB) to generate rate turbine-generator set nameplatecapacity 290 MWe (megawatts of electricity) is 732 MWh. To compensatefor the oil-to-steam heat exchanger efficiency, which may be for example92% resulting in some heat energy loss' in the heat exchanger, the SF toPB energy required is actually higher or 796 MWh (SF2PB) to deliver the732 MWh of energy needed by the PB. Since there is more thermal energyavailable from the SF than required by the PB during this generatingHour, the PB energy requirements can be met completely by the SF withoutextracting thermal energy reserves from TES. Accordingly, TES to PBenergy draw (TES2PB) is zero during generating Hour 4361 (data point 17in FIG. 16). Furthermore, the SF is producing more thermal energy thanrequired by the PB. Therefore, the excess thermal energy of 115 MWh (911MWh available−796 MWh required by PB) may be stored in and added to theTES reserve for later use during a future generating hour.

It also bears noting in the foregoing example from FIGS. 15 and 16 thatgenerating Hour 4361 is a peak power demand period for the electricutility as indicated by the TOD=1.67% (1223 TOD-scaled). Therefore, theCSP generating plant will receive 1.67 times the full base energy ratethat the electric utility is obligated to pay the generator.Accordingly, it is more profitable for the CSP generating plant togenerate and sell power to the utility during this period than othertimes (see, e.g. Hours 4345-4351 in FIG. 15 bottom and TOD-scaled graphin FIG. 16 when the TOD is less than 0.60).

As shown in FIG. 15 (2nd Pass-Indirect Storage chart) and FIG. 16, foran exemplary 24 hour generating period, the Power Block is only operatedduring Hours 4355 through 4367 which represents thermal energy dispatchand power generation schedule that maximizes revenue to the CSPgenerating plant owners by managing and optimizing the thermal energyflow in the plant from the solar collector field to the PB and/or TES.Generating power during the remaining hourly periods would not be costeffective from a revenue standpoint. It is also notable that as a resultof the two-pass optimization process described herein, management of thethermal energy dispatch or flow and generating schedule allowed anadditional hour of generation to be added when comparing the tables inFIGS. 15 and 16 (noting that generating Hour 4354 was added from thefirst pass to the second pass).

While the foregoing description and drawings represent the preferredembodiments of the present invention, it will be understood that variousadditions, modifications and substitutions may be made therein withoutdeparting from the spirit and scope of the present invention as definedin the accompanying claims. In particular, it will be clear to thoseskilled in the art that the present invention may be embodied in otherspecific forms, structures, arrangements, proportions, sizes, and withother elements, materials, and components, without departing from thespirit or essential characteristics thereof. One skilled in the art willappreciate that the invention may be used with many modifications ofstructure, arrangement, proportions, sizes, materials, and componentsand otherwise, used in the practice of the invention, which areparticularly adapted to specific environments and operative requirementswithout departing from the principles of the present invention. Thepresently disclosed embodiments are therefore to be considered in allrespects as illustrative and not restrictive, the scope of the inventionbeing defined by the appended claims, and not limited to the foregoingdescription or embodiments.

1. A system for dispatching thermal energy in a solar power generatingplant with thermal energy storage, comprising: a solar power generatingplant comprising a solar collector field, thermal energy storage, and apower block operative to generate electricity; a dispatch controllercomprising a computer processor and computer readable medium accessibleto the processor; control logic stored on the computer readable mediumand implemented by the processor, the processor when implementing thecontrol logic being operative to generate an optimized thermal energydispatch schedule which controls operation of the generating plant byperforming a combination of direct thermal energy storage and indirectthermal energy storage simulations, the processor implementing thedispatch schedule and controlling flow of thermal energy through thegenerating plant in accordance with the dispatch schedule.
 2. The systemof claim 1, wherein the flow of thermal energy through the generatingplant is controlled by the processor automatically performing at leastone of a function selected from the group consisting of opening athermal energy flow conduit, closing a thermal energy flow conduit,starting a thermal energy flow pump, and stopping a thermal energy flowpump.
 3. The system of claim 1, wherein the processor is furtheroperative to: retrieve operating data for the generating plant; generatea first thermal energy dispatch schedule by analyzing the operating datausing a direct thermal energy storage simulation; generate a secondthermal energy dispatch schedule by analyzing results from the firstthermal energy dispatch schedule using an indirect thermal energystorage simulation; modify the first thermal energy dispatch schedule byanalyzing results from the second thermal energy dispatch schedule usingthe direct thermal energy storage simulation to generate a modifiedfirst thermal energy dispatch schedule; modify the second thermal energydispatch schedule by analyzing results from the modified first thermalenergy dispatch schedule using the indirect thermal energy storagesimulation to generate a final second thermal energy dispatch schedule;and control the dispatch of thermal energy in the generating plantaccording to the final second thermal energy dispatch schedule.
 4. Thesystem of claim 1, wherein the processor uses weather forecasts andfinancial data relative to an identified operating window period of timeto perform the simulations.
 5. The system of claim 1, wherein the flowof thermal energy is characterized by the flow of a heat transfer fluidthrough the generating plant.
 6. The system of claim 5, wherein theprocessor is further operative to route the flow of the heat transferfluid from the solar collector field to at least one of the thermalenergy storage or the power block.
 7. The system of claim 1, wherein theprocessor is further operative to determine an optimum operating windowfor the generating plant by examining weather prediction data andrevenue profile data.
 8. The system of claim 1, wherein the indirectthermal energy simulation includes the processor determining if anythermal energy available from the solar collector field is greater thanthermal energy requirements of the power block for a preselected periodof time and at least one of either drawing thermal energy from thethermal energy storage to the power block or drawing thermal energy fromthe solar collector field for the period of time.
 9. The system of claim1, wherein the direct thermal energy storage simulation includes theprocessor selecting a plurality of generating hours ranked by mostprofitable to least profitable hour based on the relative revenue valuesfor every generating hour in which there is sufficient thermal energy inthermal storage to operate the power block.